7 papers
Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection
Weihan Cai, Hao Tan, Zichang Tan +2
Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, subs…
Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
Hao Tan, Jun Lan, Zichang Tan +7
Veritas++ introduces a perception‑enhanced framework for detecting AI‑generated images by training models to capture fine‑grained visual details, semantic anomalies, and pixel‑leve…
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
Senyuan Shi, Hao Tan, Zichang Tan +4
The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Image Detection (SID) methods.…
ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models
Yingxin Lai, Zitong Yu, Jun Wang +3
Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating textual rationales for forgery detection. However, processing dense visual sequence…
Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Hao Tan, Jun Lan, Zichang Tan +7
Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from…
VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
Hao Tan, Jun Lan, Senyuan Shi +6
The growing capability of video generation poses escalating security risks, making reliable detection increasingly essential. In this paper, we introduce VideoVeritas, a framework…